Local AI operations console for supervised agent work.
Run Hecate on your machine between AI clients, model providers, coding agents, and workspace tools so project work can be coordinated, routed, approved, traced, and reviewable.

Status: public alpha. Hecate is useful today for model-provider routing, Hecate Chat, External Agent sessions, project-scoped work, approvals, artifacts, usage, and observability. It is not production-stable infrastructure yet: workflow runbooks, richer agent profiles, browser QA, and sandbox hardening are still design or early-alpha work. Read known limitations before depending on it.

Contents

• What Hecate Is
• System Shape
• Current Capabilities
• Quick Start
• Use The Console
• Project, Context, And Memory Flow
• Architecture And Docs
• Status And Roadmap
• Contributing
• License

What Hecate Is

Hecate is a local AI operations console for running, supervising, and coordinating AI work. It combines a model gateway, chat workspace, task runtime, external-agent console, project orchestration, project context and memory, approval gates, artifacts, usage, and OpenTelemetry traces into one operator surface.

Hecate is local-first in the operational sense: the runtime and UI run on your machine, Hecate-owned state is stored locally, and the gateway binds to loopback by default. It is not local-only: you can route to cloud providers and supervise external coding-agent CLIs that use their own accounts.

The short version:

• Gateway: one local API for OpenAI-compatible Chat Completions, Anthropic-shaped Messages, model discovery, failover, rate limits, provider health, and usage visibility.
• Console: a React operator UI for Chats, Connections, Tasks, Projects, Usage, Observability, and Settings.
• Runtime: queued task runs, tool-calling agent_loop, approvals, per-call sandbox policy, artifacts, retries, resumes, and event streams.
• External Agent supervision: long-lived local ACP sessions for coding-agent CLIs, with readiness checks, approvals, adapter diagnostics, and Git diff review.
• Project orchestration: durable project identity, roles, work records, assignments, handoffs, activity health, project-scoped memory, context packet snapshots, project skill metadata, and explicit memory promotion.
• Evidence: traces, route reports, task artifacts, diffs, logs, screenshots where available, and final run output close to the decision that produced it.

The product goal is not just to make model calls. It is to give the operator a single place to coordinate project-scoped agent work and understand what is happening, what context it used, what it changed, what it cost, what needs approval, and where the evidence lives.

System Shape

flowchart LR
    Operator["Operator"] --> Console["Browser / desktop console<br/>served by Hecate"]
    APIClients["Compatible API clients<br/>SDKs · tools · local apps"]

    subgraph HecateProcess["Local Hecate runtime process"]
        direction LR
        HTTP["Loopback HTTP server<br/>embedded UI assets<br/>/v1 · /hecate/v1"]
        Gateway["Model gateway<br/>routing · failover · usage"]
        TaskRuntime["Hecate task runtime<br/>queue · agent_loop<br/>retry/resume"]
        AgentSupervisor["External Agent supervisor<br/>ACP sessions · diagnostics"]
        State["Local state<br/>chats · projects · memory<br/>tasks · settings"]
        Evidence["Evidence + observability<br/>approvals · artifacts · events<br/>route reports · trace export"]

        HTTP --> Gateway
        HTTP --> TaskRuntime
        HTTP --> AgentSupervisor
        HTTP --> State
        Gateway --> Evidence
        TaskRuntime --> Evidence
        AgentSupervisor --> Evidence
        State --> Evidence
    end

    Providers["Cloud + local model providers"]
    Tools["Sandboxed workspace tools<br/>WorkspaceFS · ProcessRunner · GitRunner"]
    ExternalCLIs["External Agent CLIs<br/>own accounts · own runtime"]

    Console --> HTTP
    APIClients --> HTTP
    Gateway --> Providers
    TaskRuntime --> Tools
    AgentSupervisor --> ExternalCLIs

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The runtime is deliberately boring in the good way: request handling, routing, task execution, approvals, artifacts, and telemetry are all ordinary subsystems with memory, SQLite, and Postgres storage parity where persistence matters.

Current Capabilities

Surface	What works today
Model gateway	OpenAI-compatible Chat Completions, Anthropic-shaped Messages, streaming, vision, model discovery, provider health, failover, retry, usage events, and custom OpenAI-compatible endpoints.
Connections	Cloud presets plus Ollama, LM Studio, LocalAI, llama.cpp-compatible servers, local discovery, health checks, credentials, external-agent readiness, and durable approval grants.
Chats	Direct model turns, tools-on task-backed turns, queued prompts, task/run/trace links, inline approvals, inline MCP Apps views, context packet snapshots, project-aware history, and workspace changes with rich per-file diffs.
Projects	Durable project identity, roots, context-source metadata, project activity, work items, assignments, handoffs, project memory entries, and memory candidates that require explicit operator promotion.
Tasks	Native agent_loop runs, queue/lease execution, blocking approvals, streamed activity, artifacts, retry/resume, stale-run recovery, MCP tool/App integration, MCP probe, and MCP registry discovery.
External Agent	Supervised local ACP sessions for Codex, Claude Code, Cursor Agent, and Grok Build, including readiness/version checks, prompt-first approvals, adapter diagnostics, cancellation, and Git diff inspect/revert. External agents keep their own accounts/billing.
Observability	OpenTelemetry traces/metrics/logs, response trace headers, local trace view, route reports, runtime stats, timing, token usage, and provider-reported cost where available.
Desktop app	Native bundles run the Hecate runtime as a sidecar. macOS Apple Silicon is launch-tested; Linux and Windows bundles are CI-built but still experimental.
Sandbox policy	WorkspaceFS boundaries, ProcessRunner/GitRunner seams, env sanitisation, output caps, timeouts, and bwrap / sandbox-exec wrappers where available. This is not container-level isolation.

Design direction that is not yet a runtime contract:

• Named workflow modes such as review, investigate, qa, ship, security-audit, and design-review.
• Browser-backed QA evidence and design review.
• Richer agent profiles and preset workflows.
• Broader context-window management and external memory provider selection.
• A first-class workflow/runbook API if the v0 experiments prove valuable.

Quick Start

Choose the path that matches how you want to run Hecate.

Path	Best for
Desktop app	macOS personal use on your laptop with optional embedded workspace terminal support. No Docker required. Linux/Windows bundles are experimental.
Docker	Local container, scripted local deploys, and the safer Linux/Windows alpha path today.
From source	Contributors and local development.

Desktop app

Download the current alpha from hecate.sh or from the versioned GitHub Release assets below:

Platform	Bundle
macOS (Apple Silicon)	Hecate_0.2.0-alpha.4_aarch64.dmg
Linux x86_64	Hecate_0.2.0-alpha.4_amd64.deb or Hecate_0.2.0-alpha.4_amd64.AppImage
Windows x86_64	Hecate_0.2.0-alpha.4_x64_en-US.msi

Open the bundle and launch Hecate. The app starts the bundled runtime on a private loopback port, waits for it to become healthy, and opens the operator UI automatically. State lives in the platform data dir:

• macOS: ~/Library/Application Support/sh.hecate.app/
• Windows: %APPDATA%\sh.hecate.app\
• Linux: ~/.local/share/sh.hecate.app/

macOS release bundles are signed and notarized. Linux and Windows bundles are published by CI but have not yet had the same manual launch coverage. The desktop status, updater behavior, signing notes, and footguns live in Desktop app.

Docker

docker run --rm -p 127.0.0.1:8765:8765 -v hecate-data:/data \
  ghcr.io/hecatehq/hecate:0.2.0-alpha.4

Open http://127.0.0.1:8765.

The container intentionally publishes only on 127.0.0.1. If you bind it beyond loopback, put your own access control, firewall, or reverse proxy in front. See Security for the current threat model.

Pinned image tags, binary tarballs, checksums, compose examples, storage notes, and lost-token recovery live in Deployment.

From source

just dev

Local development requires Go, Bun, and the repo toolchain described in Development. First-run environment knobs live in .env.example.

Use The Console

Add a provider

On first boot, Chats is available immediately. If Hecate detects a local runtime such as Ollama or LM Studio, the first-run card can add it in one click. For manual setup, open Connections -> Add provider.

Cloud providers need an API key. Local providers need a running local server URL, usually the preset default. Custom OpenAI-compatible endpoints can be added from the same modal when the preset catalog is not enough.

After a provider is saved, Hecate discovers models and the Chats picker becomes routable. The full provider catalog, env bootstrapping, custom-endpoint walk-through, and credential rotation live in Providers.

Chat with or without tools

Hecate Chat keeps direct model turns and tools-on task-backed turns in one transcript. Tools off sends through the gateway. Tools on uses the task runtime with approvals, artifacts, sandbox policy, and traces.

If the selected model cannot call tools, Hecate keeps the chat usable as direct model chat and makes the tools-unavailable state visible.

Review workspace changes

Workspace changes sit beside the chat as session context. You can inspect the current Git diff, filter changed files, copy patches, and discard selected files without digging through transcript noise.

Supervise External Agents

External Agent sessions run through local ACP-compatible CLIs. Hecate supervises the session but does not proxy or pool those vendors' credentials.

Approvals surface as blocking operator prompts before gated actions can proceed.

See Chat sessions, Agent runtime, and External Agents for the deeper contracts.

Project, Context, And Memory Flow

The newer Hecate shape starts with projects. A project is the durable local identity for a work area: code, research, writing, design, ops, planning, or support. A project can start without a workspace; a workspace is the concrete filesystem root used later by a chat, task, or external-agent session when local files matter.

flowchart LR
    Project["Project<br/>stable local identity"] --> Roots["Workspace roots"]
    Project --> Sources["Project sources<br/>URLs · notes · files · guidance"]
    Project --> Memory["Project memory<br/>operator-approved entries"]
    Project --> Work["Work items · assignments · handoffs"]

    Roots --> Session["Chat · task · external-agent session"]
    Sources --> Packet["Context packet snapshot"]
    Memory --> Packet
    Work --> Packet
    Session --> Packet
    Packet --> Call["Model or adapter call"]

    Session --> Artifacts["Artifacts · diffs · traces · reports"]
    Artifacts --> Candidates["Memory candidates"]
    Candidates --> Approval["Operator review"]
    Approval --> Memory

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Important boundaries:

• Context packets snapshot what Hecate assembled for a call. They are audit evidence, not durable memory by themselves.
• Project memory is explicit operator-approved context. Hecate does not write memory automatically.
• Project sources are provenance and source metadata first; Hecate does not fetch source URLs or include source bodies unless a supported context policy path explicitly does so.
• Memory candidates can be proposed by chats, tasks, handoffs, or future workflows. They stay out of context until the operator promotes them.
• External-agent private memory stays outside Hecate unless the operator imports or writes Hecate memory explicitly.

Read the implemented contract in Runtime API, then the design records for Projects, Context assembly, Agent memory, and Workflow runbooks v0.

Architecture And Docs

The full docs index lives at docs/README.md. Start with the bucket that matches your job.

You are...	Start here
Running Hecate locally	Desktop app, Deployment, Security, Providers
Calling Hecate from a client	Runtime API, Chat sessions, Agent runtime, Events
Building coding-agent integrations	External Agents, MCP integration, Events
Changing the codebase	Architecture, Development, Release, docs-ai
Planning future runtime behavior	Design records, especially the proposal/accepted/candidate bucket before implementation starts.

Runtime references:

• Runtime API - Hecate-native endpoints, task lifecycle, approvals, streaming, projects, memory, work items, and handoffs.
• Agent runtime - agent_loop, tools, costs, retry-from-turn, stdout/stderr, and system prompt layers.
• Chat sessions - transcript segments, direct turns, task-backed turns, queued prompts, context packets, and External Agent chats.
• Events - run-event names, payloads, and SSE replay.
• Telemetry - OpenTelemetry spans, metrics, logs, trace headers, local trace view, and retention.
• Sandbox - subprocess boundaries, policy validation, env sanitisation, output caps, timeouts, and OS wrappers.

Operator guides:

• Providers - provider presets, custom endpoints, credentials, model discovery, health, and circuit breaking.
• Security - local-first threat model, workspace safety, approvals, secrets, and advisory handling.
• Known limitations - the plain-language alpha boundary.

Status And Roadmap

Hecate is public-alpha software. The fastest-moving areas are project-scoped work, memory/context visibility, External Agent ergonomics, desktop packaging, workflow runbook experiments, and sandbox hardening.

Near-term design direction:

1. Keep projects, context packets, memory, artifacts, approvals, and traces as the shared substrate for all agent work.
2. Prototype one report-only qa workflow using existing task runs before adding a standalone workflow engine.
3. Start browser support with conservative evidence capture and explicit state isolation.
4. Promote successful workflow lessons only as memory candidates with provenance and operator approval.

The broader alpha-to-beta gate lives in Beta roadmap. Proposed, accepted, candidate, implemented, and parked design records live in Design records.

Contributing

See CONTRIBUTING.md. If you work with an AI assistant, start with AGENTS.md; the provider-neutral guidance layer lives in docs-ai.

License

MIT. See LICENSE.